We propose a multiagent case-based learning
(MCBL) framework in which agents learn cases to over-ride
default behavioral rules. When the actual outcome
of the action of an agent using its behavioral rules
is not consistent with the expected outcome based on
the model the agent has of other agents, the agent
recognizes that a conflict has occurred and that its behavior
is not appropriate in that situation. For those
situations, the agent learns exceptions to its behavioral
rules that are likely to prevent future conflicts. Agents
follow their behavioral rules except when a learned case
suggests alternative actions. Through this process, the
agents dynamically evolve a behavior that is suited for
the group in which it is placed.

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